Technological solutions regarding automated sorting of food according to their quality parameters are of great interest to food industry. In this regard, automated sorting of fish rest raw materials remains as one of the key challenges for the whitefish industry. Currently, the sorting of roe, milt, and liver in whitefish fisheries is done manually. Automated sorting could enable higher profitability, flexibility in production and increase the potential for high value products from roe, milt and liver that can be used for human consumption. In this study, we investigate and present a solution for classification of Atlantic cod (Gadus morhua) roe, milt and liver using visible and near-infrared hyperspectral imaging. Recognition and classification of roe, milt and liver from fractions is a prerequisite to enabling automated sorting. Hyperspectral images of cod roe, milt and liver samples were acquired in the 400–2500 nm range and specific absorption peaks were characterized. Inter- and intra-variation of the materials were calculated using spectral similarity measure. Classification models operating on one and two optimal spectral bands were developed and compared to the classification model operating on the full VIS/NIR (400–1000 nm) range. Classification sensitivity of 70% and specificity of 94% for one-band model, and 96% and 98% for two-band model (sensitivity and specificity respectively) were achieved. Generated classification maps showed that sufficient discrimination between cod liver, roe and milt can be achieved using two optimal wavelengths. Classification between roe, milt and liver is the first step towards automated sorting.